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categories: ["foundations", "genetics"]categories: ["foundations", "genetics"]tags: ["machine learning", "systems biology", "simulation", "computational modelling", "single cell", "foundation models"]tags: ["machine learning", "systems biology", "simulation", "computational modelling", "single cell", "foundation models"]summary: "Computational models that simulate a cell's molecular behaviour, from complete cell cycles of minimal bacteria to learned predictions of how human cells respond to perturbation."summary: "Computational models that simulate a cell's molecular behaviour, from complete cell cycles of minimal bacteria to learned predictions of how human cells respond to perturbation."updated: "2026-10-29"updated: "2026-10-30"humanEvidence: "Complete whole-cell models exist only for minimal bacteria; learned models of human cell lines predict part of the transcriptional response to gene silencing, and none is validated as a model of a whole human cell."humanEvidence: "Complete whole-cell models exist only for minimal bacteria; learned models of human cell lines predict part of the transcriptional response to gene silencing, and none is validated as a model of a whole human cell."access: "Most models and datasets are public, including Recon3D, Geneformer, scGPT and Arc's State; commercial world models are proprietary and released as previews to selected academic and industry users."access: "Most models and datasets are public, including Recon3D, Geneformer, scGPT and Arc's State; commercial world models are proprietary and released as previews to selected academic and industry users."reversibility: "context"reversibility: "context"lines 206–211 → 206–231191 unchanged lines not shown
against. [[altos-labs]] took the generalist prize in the 2025 challenge, which indicates where someagainst. [[altos-labs]] took the generalist prize in the 2025 challenge, which indicates where someof the interest is coming from.[^arc2025]of the interest is coming from.[^arc2025] A neighbouring line of work uses language-model agents to interpret perturbation data that alreadyexists rather than to predict it. Xin Jin's laboratory at Scripps Research posted a whole-brainatlas as a preprint in March 2026: an in vivo Perturb-seq screen in 74 juvenile mice thatdisrupted 1,947 disease-associated genes and profiled more than 7.7 million nuclei.[^shi2026] InOctober the laboratory, OpenAI and PerturbAI reported giving each of 1,946 perturbations from itto its own agent running OpenAI's GPT-Rosalind model, with the atlas manuscript withheld. Theagents matched or refined 48 of the 55 findings in that manuscript that the supplied data couldsupport, and the run took 32 minutes; the comparison figure of months to a year of human work isan illustrative estimate built on assumed minutes per perturbation and per paper, not ameasurement. In a blinded review by 30 doctoral-level biologists, human and agent novelty scoresfor 60 reports correlated at a Spearman coefficient of 0.61, and in a separate comparison ofmatched pairs the experts preferred the distributed reports to single-agent ones in 45 of 60judgements.[^sun2026] Among the leads were a response in cerebellar neurons to loss of Tsc2 thatloss of its partner Tsc1 did not produce, and raised expression of cholesterol-synthesis genesafter Dctn1 disruption, which OpenAI's announcement presented as possible clues to braindisease.[^sun2026][^openai2026] Both are hypotheses from mouse data that no experiment has yettested. The authors say independent datasets and prospective experiments are needed to showwhether the method generalises. Eight of the 14 authors work for OpenAI or PerturbAI, and thepreprint had not been peer reviewed as of October 2026.[^sun2026] Risks are mostly of the ordinary kind. A confident wrong prediction that redirects a researchRisks are mostly of the ordinary kind. A confident wrong prediction that redirects a researchprogramme costs years, and a model that cannot be inspected is hard to argue with. Training corporaprogramme costs years, and a model that cannot be inspected is hard to argue with. Training corporaare drawn from immortalised cell lines and from donors who are not representative, so predictionsare drawn from immortalised cell lines and from donors who are not representative, so predictionslines 281–284 → 301–30769 unchanged lines not shown
[^biohub2026b]: `statement` Biohub. "International, cross-sector collaboration commits nearly $2 billion to build foundational data for AI models to predict and treat disease." Press release, 7 October 2026. {The partners' own valuation of a total that combines new money, planned agency spending, earlier federal investment and in-kind data and computing; it does not say what the corporate funders receive.}[^biohub2026b]: `statement` Biohub. "International, cross-sector collaboration commits nearly $2 billion to build foundational data for AI models to predict and treat disease." Press release, 7 October 2026. {The partners' own valuation of a total that combines new money, planned agency spending, earlier federal investment and in-kind data and computing; it does not say what the corporate funders receive.}[^reuters2026]: `news` Hu, K. "US government, Google join Zuckerberg-backed Biohub in $1.8 billion push for AI biology data." Reuters, 7 October 2026. {Reports the embargo arrangement and the timelines as Biohub's head of science described them; the length of the embargo is not given.}[^reuters2026]: `news` Hu, K. "US government, Google join Zuckerberg-backed Biohub in $1.8 billion push for AI biology data." Reuters, 7 October 2026. {Reports the embargo arrangement and the timelines as Biohub's head of science described them; the length of the embargo is not given.}[^arc2025]: `statement` Arc Institute. "Arc Institute's first virtual cell model: State," June 2025; and "Virtual Cell Challenge 2025 Wrap-Up: Winners and Reflections," December 2025. {The institute's own accounts: the State training figures and the challenge prize results are reported by the party that built one and ran the other.}[^arc2025]: `statement` Arc Institute. "Arc Institute's first virtual cell model: State," June 2025; and "Virtual Cell Challenge 2025 Wrap-Up: Winners and Reflections," December 2025. {The institute's own accounts: the State training figures and the challenge prize results are reported by the party that built one and ran the other.}[^shi2026]: `preprint` Shi, T. et al. "Genome-scale functional mapping of the mammalian whole brain with in vivo Perturb-seq." bioRxiv, 2026. doi:10.64898/2026.03.16.711480. {Loss-of-function edits in juvenile mice at a single timepoint with RNA readout only; the authors note AAV tropism leaves out some cell types.}[^sun2026]: `preprint` Sun, A. et al. "Distributed AI agents enable comprehensive discovery of biological insights at scale." bioRxiv, 2026. doi:10.64898/2026.10.15.759436. {Evaluated on one mouse atlas built by the same laboratory; none of the proposed mechanisms was tested experimentally in the study.}[^openai2026]: `statement` OpenAI. "Using thousands of AI agents to uncover new leads in biological data." Company blog post, 21 October 2026. {A co-author's own account; it reports the distributed agents' literature retrieval as more than 700 references, where the preprint reports about 711 times the single agent's 11.} removed, struck through added, underlinedLine numbers count the serialised markdown of each revision, frontmatter included.
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